Kimi K2.5 OpenCode: Complete Integration Guide for AI Development

# Sign up for Moonshot AI API access
export KIMI_API_KEY="your-api-key-here"

# Verify connectivity
curl -X POST https://api.moonshot.ai/v1/chat/completions \
  -H "Authorization: Bearer $KIMI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kimi-k2.5",
    "messages": [{"role": "user", "content": "Hello, Kimi!"}]
  }'

// .opencode/config.json
{
  "ai_provider": {
    "name": "kimi",
    "model": "kimi-k2.5",
    "api_key": "${KIMI_API_KEY}",
    "base_url": "https://api.moonshot.ai/v1",
    "context_window": 256000,
    "max_agents": 100
  },
  "features": {
    "code_completion": true,
    "code_review": true,
    "documentation": true,
    "test_generation": true,
    "refactoring": true
  },
  "agent_swarm": {
    "enabled": true,
    "auto_deploy": true,
    "coordination_mode": "parallel"
  }
}

# Install via VS Code marketplace
code --install-extension moonshot-ai.kimi-code

// settings.json (example for extensions that support OpenAI-compatible providers)
{
  "openai.apiKey": "${env:KIMI_API_KEY}",
  "openai.baseUrl": "https://api.moonshot.ai/v1",
  "openai.model": "kimi-k2.5"
}

// config.json for Continue.dev
{
  "models": [
    {
      "title": "Kimi K2.5",
      "provider": "openai",
      "model": "kimi-k2.5",
      "apiBase": "https://api.moonshot.ai/v1",
      "apiKey": "${KIMI_API_KEY}",
      "contextLength": 256000,
      "completionOptions": {
        "maxTokens": 8192,
        "temperature": 0.3
      }
    }
  ],
  "tabAutocompleteModel": {
    "title": "Kimi K2.5 Autocomplete",
    "provider": "openai",
    "model": "kimi-k2.5",
    "apiBase": "https://api.moonshot.ai/v1",
    "apiKey": "${KIMI_API_KEY}"
  }
}

// .continue/commands.json
{
  "commands": [
    {
      "name": "swarm-review",
      "prompt": "Deploy Agent Swarm to review this code for bugs, security issues, and performance optimizations. Provide specific recommendations.",
      "model": "kimi-k2.5"
    },
    {
      "name": "visual-generate",
      "prompt": "Generate React component code from the provided image or description. Include TypeScript types and styled-components.",
      "model": "kimi-k2.5"
    },
    {
      "name": "context-aware-refactor",
      "prompt": "Analyze the entire codebase (up to 256K tokens) and suggest refactoring opportunities for better architecture.",
      "model": "kimi-k2.5"
    }
  ]
}

# Using OpenRouter with Kimi K2.5
import openai

client = openai.OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key="$OPENROUTER_API_KEY"
)

response = client.chat.completions.create(
    model="moonshotai/kimi-k2.5",
    messages=[
        {"role": "system", "content": "You are a helpful coding assistant."},
        {"role": "user", "content": "Explain this code: def fib(n): return n if n < 2 else fib(n-1) + fib(n-2)"}
    ],
    extra_headers={
        "HTTP-Referer": "https://your-app.com",
        "X-Title": "Your App Name"
    }
)

print(response.choices[0].message.content)

# .github/workflows/kimi-code-review.yml
name: Kimi K2.5 Code Review

on:
  pull_request:
    types: [opened, synchronize]

jobs:
  ai-review:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
        uses: actions/checkout@v4

      - name: Generate AI Review Summary
        env:
          KIMI_API_KEY: ${{ secrets.KIMI_API_KEY }}
        run: |
          DIFF="$(git diff --unified=0 origin/${{ github.base_ref }}...${{ github.sha }} | head -c 120000)"
          jq -n \
            --arg diff "$DIFF" \
            '{
              model: "kimi-k2.5",
              messages: [
                {role: "system", content: "You are a senior code reviewer. Return concise findings only."},
                {role: "user", content: ("Review this PR diff and list concrete risks:\n\n" + $diff)}
              ]
            }' > request.json

          curl -sS https://api.moonshot.ai/v1/chat/completions \
            -H "Authorization: Bearer $KIMI_API_KEY" \
            -H "Content-Type: application/json" \
            -d @request.json > kimi_review.json

# .gitlab-ci.yml
stages:
  - review

kimi_code_review:
  stage: review
  image: alpine:3.20
  variables:
    KIMI_API_KEY: $KIMI_API_KEY
    KIMI_MODEL: kimi-k2.5
  script:
    - apk add --no-cache curl jq git
    - DIFF="$(git diff --unified=0 "$CI_MERGE_REQUEST_TARGET_BRANCH_SHA" "$CI_COMMIT_SHA" | head -c 120000)"
    - |
      jq -n --arg diff "$DIFF" '{
        model: "kimi-k2.5",
        messages: [
          {role: "system", content: "You are a senior code reviewer. Return concise findings only."},
          {role: "user", content: ("Review this MR diff and list concrete risks:\n\n" + $diff)}
        ]
      }' > request.json
    - |
      curl -sS https://api.moonshot.ai/v1/chat/completions \
        -H "Authorization: Bearer $KIMI_API_KEY" \
        -H "Content-Type: application/json" \
        -d @request.json > kimi_review.json
  rules:
    - if: $CI_PIPELINE_SOURCE == "merge_request_event"

# Pseudo-code: orchestrate parallel coding tasks with your own worker pool
import asyncio

features = [
    {"name": "authentication", "priority": "high"},
    {"name": "dashboard", "priority": "high"},
    {"name": "notifications", "priority": "medium"},
    {"name": "analytics", "priority": "medium"}
]

async def implement_feature(feature):
    # Call Kimi API with repo context + feature spec, then open PR
    return feature["name"]

results = asyncio.run(asyncio.gather(*(implement_feature(f) for f in features)))

# Pseudo-code: multi-stage review with specialized prompts
class KimiCodeReviewPipeline:
    def __init__(self):
        self.review_roles = [
            {"name": "syntax_checker", "specialty": "syntax"},
            {"name": "security_scanner", "specialty": "security"},
            {"name": "performance_analyzer", "specialty": "performance"},
            {"name": "style_reviewer", "specialty": "style"},
            {"name": "test_validator", "specialty": "testing"},
            {"name": "architect", "specialty": "architecture"}
        ]
    
    async def review(self, diff, full_codebase):
        # Use full 256K context for comprehensive analysis
        context = self._prepare_context(diff, full_codebase)
        
        reviews = await self._run_parallel_reviews(self.review_roles, diff, context)
        
        return self._compile_review(reviews)

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Run Kimi K2.5 cloud model
ollama run kimi-k2.5:cloud

# Or call through local Ollama API
curl http://localhost:11434/api/chat \
  -d '{"model":"kimi-k2.5:cloud","messages":[{"role":"user","content":"Hello!"}]}'

{
  "ai_provider": {
    "name": "ollama",
    "model": "kimi-k2.5:cloud",
    "base_url": "http://localhost:11434",
    "local": false,
    "features": {
      "agent_swarm": true,
      "visual_coding": true
    }
  }
}

# Efficient context utilization
def optimize_context(files, max_tokens=256000):
    """Prioritize files for context window"""
    
    priority_order = [
        "current_file",
        "direct_imports",
        "test_files",
        "configuration",
        "related_modules"
    ]
    
    selected = []
    current_tokens = 0
    
    for priority in priority_order:
        for file in files[priority]:
            if current_tokens + file.tokens < max_tokens * 0.9:
                selected.append(file)
                current_tokens += file.tokens
    
    return selected

# Dynamic agent allocation
def calculate_optimal_agents(task_complexity, deadline):
    """Determine optimal number of agents"""
    
    base_agents = {
        "simple": 5,
        "moderate": 20,
        "complex": 50,
        "enterprise": 100
    }
    
    agents = base_agents[task_complexity]
    
    # Adjust for deadline pressure
    if deadline < timedelta(hours=4):
        agents = min(agents * 2, 100)
    
    return agents

# Cost control configuration
cost_control:
  daily_budget: 50  # USD
  alert_threshold: 0.8  # 80% of budget
  rate_limiting:
    requests_per_minute: 100
    tokens_per_minute: 1000000
  caching:
    enabled: true
    ttl: 3600  # 1 hour
    similarity_threshold: 0.95